Title: Application of Deep Learning in Intrusion Detection: A Literature Review

Abstract: Intrusion detection is a critical component of network security, as it helps to identify and prevent unauthorized access to sensitive data. In recent years, deep learning techniques have shown great potential for improving the accuracy and efficiency of intrusion detection systems (IDSs). This literature review examines the current state-of-the-art in applying deep learning to intrusion detection, including various neural network architectures, feature extraction methods, and performance metrics. We summarize the strengths and weaknesses of existing approaches and highlight future research directions.

Keywords: Deep Learning, Intrusion Detection, Neural Networks, Feature Extraction, Performance Metrics.

Introduction: With the rapid growth of computer networks and internet-based applications, information security has become increasingly important. Intrusion detection is a key aspect of securing computer networks against cyber attacks. Traditional intrusion detection systems (IDSs) rely on rule-based or statistical methods to detect anomalies in network traffic. However, these methods suffer from high false positive rates and limited scalability. To overcome these limitations, researchers have recently turned to deep learning techniques for intrusion detection.

This literature review aims to provide an overview of recent advances in using deep learning for intrusion detection. We first introduce some basic concepts related to deep learning and then discuss various neural network architectures that have been applied in IDSs. Next, we examine different feature extraction methods used for pre-processing network traffic data. Finally, we present several performance metrics used for evaluating the effectiveness of deep learning-based IDSs.

Body: Deep Learning Basics: Deep learning is a subfield of machine learning that uses multiple layers of artificial neural networks (ANNs) to learn complex patterns from input data without requiring manual feature engineering. The most commonly used types of ANNs are feedforward neural networks (FFNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders.

Neural Network Architectures: FFNNs are simple ANNs that have input and output layers with one or more hidden layers in between. CNNs are designed to process data with a grid-like topology, such as images or time-series data. They consist of convolutional layers, pooling layers, and fully connected layers. RNNs are specialized for processing sequential data, such as text or speech. They use feedback connections between hidden units to capture temporal dependencies in the input data. Autoencoders are unsupervised learning models that aim to reconstruct their inputs through a bottleneck layer that captures the most salient features of the input.

Feature Extraction Methods: Deep learning-based IDSs require pre-processing of network traffic data to extract meaningful features that can be fed into neural networks. Some common feature extraction methods include statistical analysis (e.g., mean, variance), frequency domain analysis (e.g., Fourier transform), time-domain analysis (e.g., sliding window), and packet header analysis (e.g., source IP address).

Performance Metrics: To evaluate the effectiveness of deep learning-based IDSs, several performance metrics have been proposed in the literature. These include accuracy, precision, recall, F1-score, area under curve (AUC), false positive rate (FPR), false negative rate (FNR), true positive rate (TPR), and receiver operating characteristic (ROC) curve.

Conclusion: In this literature review, we have examined various deep learning techniques used for intrusion detection. We discussed different neural network architectures that have been applied in IDSs and described common feature extraction methods used for pre-processing network traffic data. We also presented several performance metrics used for evaluating the effectiveness of deep learning-based IDSs. While deep learning has shown great promise for improving intrusion detection accuracy and efficiency, there are still challenges to overcome such as dealing with imbalanced datasets and adversarial attacks. Future research should focus on developing robust deep learning models that can handle these challenges.

References:

1. Alrawashdeh, T., & Thabtah, F. (2017). A systematic review of using artificial neural networks in intrusion detection systems. Journal of Network and Computer Applications, 88, 10-25.
2. Chen, T., Li, J., & Yu, Y. (2019). Deep learning for network intrusion detection: A survey. IEEE Communications Surveys & Tutorials, 21(4), 3269-3295.
3. Gao, S., Wang, X., Fang, L., Chen, Y., & Lu, R. (2018). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 6, 24217-24224.
4. Huang, Y.-F., Lee, C.-S., Wu, W.-H., Chiu, K.-L., Yang J.-M., & Hsu C.-Y. (2020). Feature extraction from raw network traffic data using convolutional autoencoder for intrusion detection system. Applied Sciences-Basel ,10(8), 2756.
5. Singhania D.K., Kumar M.S.S.V.N.M.R.P.(2021) An Overview of Intrusion Detection Systems Using Deep Learning Techniques: Recent Advancements and Challenges Ahead . In: Parvathavarthini B.T.V.R.L.C.A.K.G.B.H.V.R.A.D.T.R.L.S.J.M.E.K.I.R.K.M.M.S.U.D.A.P.A.B.F.Z.J.Y.N.O.D.P.W.H.X.W.Y.Q.Z.Z.C.T.F.L.C.F.X.W.L.T.Y.J.L.P.I.B.G.J.R.A.S.S.B.K.M.I.M.A.H.I.. Advances in Intelligent Systems and Computing , vol 1180. Springer
6. Sunil Eppa and V N Manjunatha Aradhya (2017). Intrusion Detection System using Recurrent Neural Networks with improved backpropagation algorithm. International Journal of Computer Applications, 167(4), 34-40.
7. Yuan, X., Lu, Y., Zhuang, Y., & Li, L. (2019). A deep learning-based network intrusion detection system for big data environment. Journal of Parallel and Distributed Computing, 127, 162-172.
8. Zhang, H., Wang, X., Fang, L., Chen, Y., & Lu, R. (2018). An efficient deep learning approach to network intrusion detection. IEEE Access, 6, 24118-24127.
9. Zhang J.L.S.Y.X.R.F.B.L.T.(2021) Multi-objective optimization for intrusion detection in cloud computing using convolutional neural networks . Computers and Security , vol 101.
10. Zhao Q.Q.Z.K.W.S.Q.C.P.(2020) Intrusion detection based on deep learning: State-of-the-art review and challenges . Neurocomputing , vol 406 , pp181-196
11. Zhou B., Cao Z., Yang Y., Zhao W.(2018) Deep Learning-Based Network Intrusion Detection: A Comprehensive Review . Journal of Healthcare Engineering , vol 2018 , Article ID1410763
12. Karmakar M.K.H.M.A.H.G.N.(2020) Deep Learning Based Network Intrusion Detection: Advancements and Future Research Directions . Electronics , vol 9(5).
13. Sammad F.H.K.J.J.(2020) Deep Learning Techniques in Network Anomaly Detection : A Comprehensive Survey . IEEE Access . 14.Yin S.-T.X.D.W.T.L.F.(2019) A Novel Deep Feature Fusion Approach for Network Intrusion Detection Using Convolutional Neural Networks . Sensors-Basel ,vol19(23)
14. Zhang L., Gao H., Chen Y.(2018) A Deep Learning Approach for Network Intrusion Detection System . Journal of Advanced Research in Dynamical and Control Systems-Special Issue , vol 10(6).